1. Field
Exemplary embodiments relate to a detector and a method of detecting an object using the detector.
2. Description of the Related Art
Object detection may be a foundation of an application field such as management of media contents, smart control, and the like, and also may be a significantly important technology of the application field. In particular, detection of a face of a human may be widely used in an application field such as identification, smart control, smart cameras, and the like, as an important part of the object detection.
In the related arts, object detection may be performed using a single detector. As examples of the single detector, a Haar detector, a Scale Invariant Feature Transform (SIFT) detector, a Modified Census Transform (MCT) detector, a Non-negative Matrix Factorization (NMF), and the like may be used. FIGS. 1A, 1B, and 1C are diagrams respectively illustrating Haar edge characteristics, Haar line characteristics, and Haar expansion characteristics according to a related art. The Haar edge characteristics may be used in a configuration edge characteristic detector, the Haar line characteristics may be used in a configuration line characteristic detector, and the Haar expansion characteristics may be used in a point/diagonal line characteristic detector.
The Haar characteristics may be calculated by the following Equation 1.
                                          h            ⁡                          (              x              )                                =                                    ∑                              i                ∈                R                                      ⁢                                          w                i                            ⁢                              p                i                                                    ,                            Equation        ⁢                                  ⁢        1            
where ‘R’ denotes a predetermined part area, ‘i’ denotes a pixel within an area, ‘pi’ denotes a gray scale value of the pixel, and ‘wi’ denotes a weight of the pixel, which is represented as Equation 2 below.
                                          ∑                          i              ∈              R                                ⁢                      w            i                          =        0.                            Equation        ⁢                                  ⁢        2            
A weight sum of the Haar characteristic values may be a discrimination basis for the Haar detector to select a detected sample. Also, the calculated Haar characteristic value may be obtained simply using an image integration method.
FIG. 2 illustrates MCT characteristics of a 3×3 net, according to a related art.
In FIG. 2, each net size is identical to the size of the other nets, and the net may be a single pixel, or a rectangle or a square of which a plurality of pixels are combined while showing MCT characteristics based on the 3×3 net of the related art. FIG. 3 illustrates exemplary MCT characteristics included in a detector according to the related art. In this instance, a white net is denoted as having characteristic value higher than an average value, and a black net is denoted as having characteristic value lower than the average value.
As for the MCT characteristic value, a sum of pixel values of respective nets based on the 3×3 net may be a characteristic value of a net. With a comparison between the characteristic value of the net and an average value of characteristic values of all nets, ‘0’ or ‘1’ may be obtained. Since integers of ‘0’ to ‘511’ may be obtained based on a compared result of nine nets, the MCT characteristic value may be obtained using an index.
An index address of the MCT characteristics value may be calculated by the following Equation 3.
                                          m            ⁡                          (              x              )                                =                                    ∑                              i                =                1                            9                        ⁢                                          s                ⁡                                  (                                                            g                      i                                        -                                          g                      c                                                        )                                            ×                              2                i                                                    ,                            Equation        ⁢                                  ⁢        3            
where ‘gi’ denotes an i-th element value of the 3×3 net, and ‘gc’ denotes an average value of the net, which is represented as Equation 4.
                              g          c                =                              1            9                    ⁢                                    ∑                              i                =                1                            9                        ⁢                                          g                i                            .                                                          Equation        ⁢                                  ⁢        4            
s(x) denotes a function having two values, which is represented as Equation 5.
                              s          ⁡                      (            x            )                          =                  {                                                    1                                                              x                  ≥                  0                                                                                    0                                                              x                  <                  0.                                                                                        Equation        ⁢                                  ⁢        5            
The MCT characteristics values may be obtained by a comparison between each element value and the average value, and may be resistant to noise and illumination.
Also, each element of the 3×3 net may be a single pixel, or a rectangular area or a square area. Similar to the Haar characteristics, a weight sum of the MCT characteristics values may be a discrimination basis of the MCT detector to select a detected sample. In addition, the index address of the MCT characteristic values may be simply obtained using the image integration method.
However, in the related arts, all of the Haar detector, the MCT detector, the SIFT detector, and the NMF detector may have a significant limitation due to a single detector adapted by each of the Haar detector, the MCT detector, the SIFT detector, and the NMF detector, and thus, it is difficult to implement a rapid and accurate object detection. For example, as can be seen from FIGS. 1A to 3, the Haar detector may have a simple structure, and may be operated with a relatively rapid speed, however, many Haar detectors may need to be combined for the purpose of classifying complex characteristics, resulting in significantly deteriorated efficiency. The SIFT detector may have a complex structure, and may be operated in a relatively slow speed, and thus it is difficult to implement a current rapid object detection. In addition, the MCT detector, although convenient for classifying relatively complex characteristics, may have a relatively complex structure and may be operated with a relatively slow speed.
Accordingly, there is a desire for a method that will overcome problems associated with the single detector and that will perform rapid and accurate object detection.